mlsanityData to 7 Oct 2026

Fair Performance Metric Elicitation

arXiv 2006.12732 ↗First posted 23 Jun 2020stat.ML cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics that best reflect implicit preferences. The use of metric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context, and population at hand. Specifically, we propose a novel strategy to elicit group-fair performance metrics for multiclass classification problems with multiple sensitive groups that also includes selecting the trade-off between predictive performance and fairness violation. The proposed elicitation strategy requires only relative preference feedback and is robust to both finite sample and feedback noise.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 1; 2025: 3; 2026: 1.

13 papers.

Sort:

By area: